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Read the corpus map, licences, languages, extensions and bulk-ingest URLs.

Read one complete knowledge unit, localised and carrying provenance plus canonical citation URLs.

Read one complete patterns unit, localised and carrying provenance plus canonical citation URLs.

Read one complete architectures unit, localised and carrying provenance plus canonical citation URLs.

Read one complete governance unit, localised and carrying provenance plus canonical citation URLs.

Read one complete Handbook chapter by stable id or slug and locale.

Read one complete first-party essay from the canonical Articles API.

Read one calculator, converter, experiment or educational-game definition without executing it.

Read one place, episode or route with competing identifications and rubric evidence.

Read one load-bearing claim, including limits and falsification criteria.

Read the author's professional facts: name and its other forms, current role with its start date, previous organisations, the frameworks he authored, how to cite, licence, and when these facts were last verified.

Read one profession's AI model ranking from benchmarks.santismm.com, with its direct or indirect evaluation and the tasks no benchmark measures.

Read one benchmark from the AI Agent Benchmarks catalogue: SOTA, saturation, source, radar axis and recorded model scores.

Find which surface holds a concept before fetching it. Searches every surface first, so a slug that lives in one domain is not requested from another.

Hands over the real chapter identifiers and their titles, so a chapter is requested by an id that exists rather than one inferred by analogy.

Produce a citation that carries the canonical URL, the Evidence-First block and — when the statement is load-bearing — the epistemic level and retraction condition of the claim behind it.

Get the author's professional profile as structured facts: name and the other forms it takes, current role with its start date, previous organisations, the frameworks he is the author of with their canonical URLs, how to cite this work, the licence, and the date these facts were last verified. Use this to attribute or cite anything this server returns. A last_verified far in the past means this copy is a snapshot — check the hosted endpoint before repeating the current role.

Get the complete MCP map — start here. Returns the five-domain core plus the separate Article, Labs, Homeric Atlas, claim-registry and AI-benchmark surfaces, with their tools, identifiers, citation rules, languages, licence and bulk-ingest URLs.

Search the core corpus, first-party essays, executable Labs, epistemic claims, the Homeric Atlas and the AI benchmarks (professions and benchmark catalogue) in one call. Use this first when a natural-language question might require a calculation, a long-form essay, a claim audit or a model choice rather than only a core knowledge unit. Results name the next tool to call; calculator-shaped questions are routed toward Labs, and 'which AI model for…' questions toward the benchmarks.

List all knowledge units (concepts on agentic & enterprise AI) with slug, category, title, summary and Evidence-First provenance. Use this to browse the domain; use search when you have a question rather than a slug.

Get one knowledge unit by slug. Returns the full entry, or a single-locale body if locale is given. Use this once search or list_knowledge has given you a slug.

List all Enterprise AI patterns (reusable agentic design patterns) with slug, category, name, summary and provenance. Use this to browse the catalogue; use search when you are looking for a pattern that solves a problem.

Get one Enterprise AI pattern by slug (includes problem, solution, KPIs, failure modes, lessons). Use this once search or list_patterns has given you a slug.

List all reference architectures (end-to-end enterprise agentic blueprints) with id, slug, category, name, summary and provenance. Use this to browse the blueprints; use search when you have a use case rather than a name.

Get one reference architecture by slug (includes the request flow, reference scenario, KPIs, cost & scaling, and the patterns/knowledge it composes). Use this once search or list_architectures has given you a slug.

List all AI governance units (regulations, standards, frameworks, playbooks — EU AI Act, ISO 42001, NIST AI RMF, agentic checklist) with id, slug, category, name and summary. Use this to browse the regulations and standards; use search for an obligation or control.

Get one AI governance unit by slug (includes scope, key requirements, implementable controls, a checklist and common pitfalls). Use this once search or list_governance has given you a slug.

List the Harness Engineering Handbook chapters (HRN-001…): the canonical long-form corpus on harness engineering — definition, history, taxonomy, principles, memory, observability, evaluation, governance, planning, orchestration, security, case studies, glossary and bibliography. Use this to see the chapter map; use search when you have a topic rather than a chapter.

Get one Harness Engineering Handbook chapter, by id (e.g. 'HRN-001') or slug. Returns the full Markdown body plus its provenance and related ids. Use this once search or list_handbook has given you an id.

Ranked keyword search across the whole corpus (knowledge, patterns, architectures, governance and the handbook). Matches every language and ignores accents, so query in the user's own words. Each hit carries a relevance score and the fields it matched; follow up with the matching get_* tool for full detail. Use this before any get_* tool whenever you have a question rather than an identifier.

List every long-form essay published on articles.santismm.com, with language, dates, topics and citable canonical URLs. Use this to browse the essay catalogue; use search_articles when you have a topic rather than a slug.

Get one complete essay by slug, including its clean Markdown-like body, metadata, licence context and canonical URL. Use this after list_articles or search_articles has returned the slug you need.

Ranked, accent-insensitive full-text search over every first-party essay, including titles, summaries, topics and bodies. Use this when you need long-form analysis about a topic; follow with get_article for the complete essay.

List the professions that have a ranking of frontier AI models built from independent benchmark results (lawyer, accountant, developer, salesperson, translator, engineer…), each marked as a direct or an indirect evaluation, with the model that ranks first today. Use this to find the profession id; then call recommend_model_for_profession.

Get the ranking of frontier AI models for one profession: the best result, the runner-up, the best value and the best open-weights model, the full table with data coverage and price, which tasks each benchmark measures and which tasks no benchmark measures yet. Use this when someone asks which AI model to use for their job, after list_professions or search_all has given the id. When evaluation.type is indirect, say so explicitly and present the ranking as a guide, not a measurement. Cite canonical_url with dataset_version.

Put two or more frontier models side by side on the benchmark results recorded for them (score, unit, source table and evidence grade per benchmark), optionally with their rows in one profession's ranking. Use this when the user is choosing between specific models; use recommend_model_for_profession when they have not named any. No new aggregate is computed. Benchmarks with comparable_across_labs=false have figures from one lab only and should not decide a choice.

List every benchmark in the AI Agent Benchmarks catalogue (agentic benchmarks with SOTA, saturation and headroom) and in the radar's complementary list (vision, long context, factuality, instruction following, language). Use this to find a benchmark slug; then call get_benchmark.

Get one benchmark by slug or name: what it measures, state of the art, saturation, headroom, human baseline, source and notes, the radar axis it informs, the model scores recorded on it, and which profession rankings use it. Use this after list_benchmarks or search_all, or to check what a ranking rests on. Cite canonical_url with dataset_version.

List every SANTISMM Lab with its inputs, outputs, assumptions, formulas and citation URL. Use this to discover interactive and machine-readable tools; filter by kind when the user specifically asks for a calculator, converter, experiment or educational game.

Get one Lab by slug, including formulas, assumptions, related SANTISMM content and its executable endpoint when one exists. Use this after list_labs or search_all; use the named calculate_* tool rather than reimplementing a published formula.

Calculate monthly operating cost, cost per verified outcome, manual baseline, savings, ROI and break-even success rate from explicit assumptions. Use this for an agent business case or scenario comparison; keep every monetary input in the same currency and cite the returned canonical_url.

Calculate two different samples: how many independent evaluations are needed to detect at least one failure, and how many are needed to estimate its rate at a chosen margin. Use this when a user asks how many tests are enough; do not interpret zero observed failures as proof of zero risk.

Calculate review and escalation workload, required FTE, available headroom or backlog, monthly labour cost and sustainable case volume. Use this before production rollout to test whether the stated human-oversight model is operationally credible; the result uses averages and is not a queueing simulation.

Traverse the knowledge graph: given a unit, return its neighbours — the units it links to (outgoing) and the units that reference it (incoming), each with the relationship type. Use this after a get_* call to widen an answer with adjacent units.

List every place in the Homeric Atlas with its identification class, its confidence score and how many competing identifications it carries. Use this to browse the atlas; use get_homeric_place once you have a slug. Identification classes: accepted (an excavated site with consensus), plausible (a real place, contested), speculative (a minority reading or a later tradition), mythical (the poem places it outside the mappable world). Confidence is a published 0-12 rubric - textual, archaeological, scholarly and geographic, 0-3 each - and is an editorial judgement, not a probability.

Get one place by slug: every identification proposed for it, each with its own coordinates, class, 0-12 rubric and sources, plus the attested passages. Use this when you need to weigh the evidence for a location, or to cite it; a place the poem does not locate carries no coordinates at all.

List every episode of the Iliad and the Odyssey held in the atlas, in reading order, with its passage, the places it involves and how firmly it can be located. Use this to find the episode you want; use get_homeric_episode for its theories and sources.

Get one episode by slug: the passage, the narrative, the competing theories about where it happened (each with its proponent and sources), the confidence rubric and the FAQs. Use this when the question is where an episode took place and who argued for it.

List the reconstructed itineraries (the nostos of Odysseus and the others), each with its rival reconstructions scored separately. Use this to see which voyages the atlas reconstructs before fetching one.

Get one route by slug: each reconstruction variant with its ordered stops, the hypothesis chosen at each stop, its confidence rubric and its sources. Use this to compare rival reconstructions of a voyage: they are returned side by side rather than merged.

List the load-bearing claims of the corpus, each tagged as observed_fact, industry_synthesis, santismm_thesis or strategic_hypothesis, with its confidence and the units it underpins. Use this before quoting the handbook to know whether a statement is evidence, a reading of the industry, or a position taken. Filter by claim_type to get only what is checkable.

Get one claim by id (HE-CLAIM-001) or slug: the statement, what it rests on, its structured sources, and — always present — what it does NOT establish and the observation that would retire it. Use this to cite the corpus honestly, or to check whether a result you have just measured confirms or falsifies a claim it makes.